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Q-Learning

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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/glample/Arnold

공식 코드

Playing FPS Games with Deep Reinforcement Learning

https://github.com/golsun/deep-RL-time-series

공식 코드

Deep reinforcement learning for time series: playing idealized trading games

https://github.com/google-research/batch_rl

공식 코드

An Optimistic Perspective on Offline Reinforcement Learning

https://github.com/google-research/google-research/tree/master/experience_replay

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Revisiting Fundamentals of Experience Replay

https://github.com/google-research/google-research/tree/master/mol_dqn

공식 코드

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/google-research/look-back-when-surprised

공식 코드

Introspective Experience Replay: Look Back When Surprised

https://github.com/google-research/seed_rl

공식 코드

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

https://github.com/gpdwatkins/graph_colouring_with_RL

공식 코드

Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks

https://github.com/haarnoja/sac

공식 코드

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/haarnoja/softqlearning

공식 코드

Reinforcement Learning with Deep Energy-Based Policies

https://github.com/hanbingyan/CoinMM

공식 코드

Cooperation between Independent Market Makers

https://github.com/hanbingyan/collusion

공식 코드

Understanding algorithmic collusion with experience replay

https://github.com/haozhu10015/hiql

공식 코드

Multi-intention Inverse Q-learning for Interpretable Behavior Representation

https://github.com/heasung-kim/safe-rl-deployment-for-5g

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Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach

https://github.com/heitzjon/mt-join-query-optimization-with-drl

공식 코드

Join Query Optimization with Deep Reinforcement Learning Algorithms

https://github.com/hengruicai/djl

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Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings

https://github.com/hmhuy0/SPRINQL

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SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning

https://github.com/hsvgbkhgbv/shapley-q-learning

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SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

https://github.com/humanandmachinehearing/audiodriven_drl_for_headorientationcontrol

공식 코드

Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments

https://github.com/humemai/agent-room-env-v1

공식 코드

A Machine with Short-Term, Episodic, and Semantic Memory Systems

https://github.com/hyunghona/emu

공식 코드

Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning

https://github.com/iassael/tax-evasion-dqn

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Using deep Q-learning to understand the tax evasion behavior of risk-averse firms

https://github.com/ibrahim-elshar/LBQL_ICML2020

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Lookahead-Bounded Q-Learning

https://github.com/idea-iitd/GCOMB

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Learning Heuristics over Large Graphs via Deep Reinforcement Learning

https://github.com/idea-iitd/TANDIS

공식 코드

Task and Model Agnostic Adversarial Attack on Graph Neural Networks

https://github.com/ihuhuhu/RAC

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Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

https://github.com/ikostrikov/implicit_q_learning

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Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/illidanlab/Simulator

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Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management

https://github.com/imagry/aleph_star

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Reinforcement Learning with A* and a Deep Heuristic

https://github.com/imanolarrieta/angrybirds

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Angrier Birds: Bayesian reinforcement learning

https://github.com/improbable-ai/ter

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Topological Experience Replay

https://github.com/indujohniisc/GSQL

공식 코드

Generalized Speedy Q-learning

https://github.com/j3soon/dfac

공식 코드

DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

https://github.com/jackyk02/distributed_rl_benchmarks

공식 코드

Efficient Parallel Reinforcement Learning Framework using the Reactor Model

https://github.com/jacobha/rlc-softqbounding

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Boosting Soft Q-Learning by Bounding

https://github.com/jaraujo98/SinglePartitionAdaptiveQLearning

공식 코드

Single-partition adaptive Q-learning

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